Carlos Raymundo

Dr. Carlos Raymundo

Lima, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
orcid
PhDProfessor and Director of the Research & Development Labo...

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
9 February 2023 in Frontiers in Education
Verified
Journal Article
Open access
HCHeyul Chavez
Heyul Chavez
BCBill Chavez-Arias
Bill Chavez-Arias
SCSebastian Contreras-Rosas
Sebastian Contreras-Rosas
JAJose María Alvarez-Rodríguez
Jose María Alvarez-Rodríguez
Carlos RaymundoCarlos Raymundo
Carlos Raymundo
In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students’ academic performance based on personal information such as social status, income, address, etc. This study proposes and develops an artificial neural network model capable of determining whether a student will pass a certain class without using personal or sensitive information that may compromise student privacy. For model training, we used information regarding 32,000 students collected from The Open University of the United Kingdom, such as number of times they took the course, average number of evaluations, course pass rate, average use of virtual materials per date and number of clicks in virtual classrooms. Attributes selected for the model are as follows: 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-score. These results will help the student authorities to take measures to avoid withdrawal and underachievement.

1 September 2018 in Data & Knowledge Engineering
Verified
Journal Article
Closed access
JTJoe Tekli
Joe Tekli
Richard ChbeirRichard Chbeir
Richard Chbeir
ATAgma J.M. Traina
Agma J.M. Traina
CTCaetano Traina
Caetano Traina
KYKokou Yetongnon
Kokou Yetongnon
CKChristian Kallas
Christian Kallas


6 August 2022 in Energy Reports
Verified
Journal Article
Open access
APAlexander Pomasoncco-Najarro
Alexander Pomasoncco-Najarro
CTClaudio Trujillo-Valerio
Claudio Trujillo-Valerio
LALuis Arauzo-Gallardo
Luis Arauzo-Gallardo
Carlos RaymundoCarlos Raymundo
Carlos Raymundo
GQGrimaldo Quispe
Grimaldo Quispe
FDFrancisco Dominguez
Francisco Dominguez

26 August 2024 in Scientific Reports
Verified
Journal Article
Open access
PDF file
KGKevin Guerra
Kevin Guerra
Carlos RaymundoCarlos Raymundo
Carlos Raymundo
MSManuel Silvera
Manuel Silvera
GZGianpierre Zapata
Gianpierre Zapata
JMJavier M. Moguerza
Javier M. Moguerza
The accelerated deterioration of roads is conditioned by parameters such as climate change, poor construction, and heavy vehicle traffic. Two relevant measures to monitor the condition of a road are the International Roughness Index (IRI) and the number of functional failures in a segment, mainly potholes, since they are associated with higher risks such as accidents or damage to vehicle mechanics. In the state of the art, pothole detection or International Roughness Index (IRI) calculation algorithms are proposed, but they use vehicles designed to produce less vibration and use phones that decrease the performance of the embedded sensors. In addition, some works propose complex algorithms of higher computational load that leads to use more hardware and power consumption. In this context, the present work aims to monitor the condition of a road through low-cost dedicated sensors implemented in an urban patrolling all-terrain vehicles (ATVs), where energy consumption is optimized using low-complexity signal processing techniques for noise reduction and detection algorithms. The results show an average accuracy of 90.5% in the detection of potholes, a relative error of 8.41% in the calculation of the International Roughness Index (IRI) and an average reduction of 65.4% in the monitoring time.

1 December 2017 in Energy Procedia
Verified
Journal Article
Open access
JAJuan Arévalo
Juan Arévalo
Grimaldo Wilfredo Quispe SantivañezGrimaldo Wilfredo Quispe Santivañez
Grimaldo Wilfredo Quispe Santivañez
Carlos RaymundoCarlos Raymundo
Carlos Raymundo
The proposed Sustainable Energy Model is based on rice husk and the development of briquettes made from agricultural waste, which will increase efficiency in the domestic sector, and potentially replace conventional polluting fuels such as firewood. Large volumes of rice husks from millers are found scattered in rural agricultural areas of the San Martin region of Peru, where people are exposed daily to the emissions of polluting gases produced by burning these wastes, causing respiratory and lung diseases. Despite present circumstances, this waste has a great energetic potential that is not yet used by society, representing an opportunity to encourage socioenvironmental development and generate added value to the husk. Based on a compaction and drying process, briquettes were obtained with 4,040 kcal / kg of heat power and 80.39% combustion efficiency, allowing the little use of biofuel compared to firewood, and consequently, the utilization of this biofuel would reduce levels of deforestation. In contrast to similar projects, the sustainability of an energetic model of briquette production will be achieved when economic, environmental and social aspects are met, developing clean technologies and an efficient supply chain, from the supply of the husk to the commercialization of briquette